html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/2227#issuecomment-1468649950,https://api.github.com/repos/pydata/xarray/issues/2227,1468649950,IC_kwDOAMm_X85XidHe,2448579,2023-03-14T18:49:51Z,2023-03-14T18:54:16Z,MEMBER,"A reproducible example would help but indexing with dask arrays is a bit limited.

On https://github.com/pydata/xarray/pull/5873 it's possible it will raise an error and ask you to compute the indexer. Also see https://github.com/dask/dask/issues/4156

EDIT: your slowdown is probably because it's compuing `Sn` multiple times. You could speed it up by calling compute once and passing a numpy array to `isel`","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-1464180874,https://api.github.com/repos/pydata/xarray/issues/2227,1464180874,IC_kwDOAMm_X85XRaCK,1217238,2023-03-10T18:04:23Z,2023-03-10T18:04:23Z,MEMBER,"@dschwoerer are you sure that you are actually calculating the same thing in both cases?  What _exactly_ do the values of `slc[d]` look like? I would test thing on smaller inputs to verify. My guess is that you are inadvertently calculating something different, recalling that Xarray's broadcasting rules differ slightly from NumPy's.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-558700154,https://api.github.com/repos/pydata/xarray/issues/2227,558700154,MDEyOklzc3VlQ29tbWVudDU1ODcwMDE1NA==,2448579,2019-11-26T16:08:24Z,2019-11-26T16:08:24Z,MEMBER,"I don't know much about indexing but that PR propagates a ""new"" indexes property as part of #1603 (work towards enabling more flexible indexing), it doesn't change anything about ""indexing"". I think the dask docs may be more relevant to what you may be asking about: https://docs.dask.org/en/latest/array-slicing.html","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-533193480,https://api.github.com/repos/pydata/xarray/issues/2227,533193480,MDEyOklzc3VlQ29tbWVudDUzMzE5MzQ4MA==,1217238,2019-09-19T15:49:24Z,2019-09-19T15:49:24Z,MEMBER,"Yes, align checks `index.equals(other)` first, which has a shortcut for the same object.

The real mystery here is why `time_filter.indexes['time']` and `ds.indexes['time']` are not the same object. I guess this is likely due to lazy initialization of indexes, and should be fixed eventually by the explicit indexes refactor.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-533119743,https://api.github.com/repos/pydata/xarray/issues/2227,533119743,MDEyOklzc3VlQ29tbWVudDUzMzExOTc0Mw==,2448579,2019-09-19T13:00:40Z,2019-09-19T13:00:40Z,MEMBER,"I think align tries to optimize that case, so maybe something's also possible there?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-533036570,https://api.github.com/repos/pydata/xarray/issues/2227,533036570,MDEyOklzc3VlQ29tbWVudDUzMzAzNjU3MA==,6213168,2019-09-19T08:57:44Z,2019-09-19T08:57:44Z,MEMBER,"Can we short-circuit the special case where the index of the array used for slicing is the same object as the index being sliced, so no alignment is needed?

```python
>>> time_filter.time._variable is ds.time._variable
True
>>> %timeit xr.align(time_filter, ds.a)
477 ms ± 13.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
the time spent on that align call could be zero!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-533033540,https://api.github.com/repos/pydata/xarray/issues/2227,533033540,MDEyOklzc3VlQ29tbWVudDUzMzAzMzU0MA==,6213168,2019-09-19T08:49:32Z,2019-09-19T08:49:32Z,MEMBER,"Before #3319:
```

%timeit ds.a.values[time_filter]
158 ms ± 1.14 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit ds.a.isel(time=time_filter.values)
2.57 s ± 3.65 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit ds.a.isel(time=time_filter)
3.12 s ± 37.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
After #3319:
```
%timeit ds.a.values[time_filter]
158 ms ± 2.2 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit ds.a.isel(time=time_filter.values)
665 ms ± 6.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit ds.a.isel(time=time_filter)
1.15 s ± 1.55 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
Good job!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-532804542,https://api.github.com/repos/pydata/xarray/issues/2227,532804542,MDEyOklzc3VlQ29tbWVudDUzMjgwNDU0Mg==,1217238,2019-09-18T18:17:22Z,2019-09-18T18:17:22Z,MEMBER,"https://github.com/pydata/xarray/pull/3319 gives us about a 2x performance boost. It could likely be much faster, but at least this fixes the regression.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-532787342,https://api.github.com/repos/pydata/xarray/issues/2227,532787342,MDEyOklzc3VlQ29tbWVudDUzMjc4NzM0Mg==,1217238,2019-09-18T17:33:38Z,2019-09-18T17:33:38Z,MEMBER,"Yes, I'm seeing similar numbers, about 10x slower indexing in a DataArray. This seems to have gotten slower over time. It would be good to track this down and add a benchmark!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-532780068,https://api.github.com/repos/pydata/xarray/issues/2227,532780068,MDEyOklzc3VlQ29tbWVudDUzMjc4MDA2OA==,2448579,2019-09-18T17:14:38Z,2019-09-18T17:14:38Z,MEMBER,"On master I'm seeing

```
%timeit ds.a.isel(time=time_filter)
3.65 s ± 29.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit ds.a.isel(time=time_filter.values)
2.99 s ± 15 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit ds.a.values[time_filter]
227 ms ± 6.59 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```

Can someone else reproduce?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-454162334,https://api.github.com/repos/pydata/xarray/issues/2227,454162334,MDEyOklzc3VlQ29tbWVudDQ1NDE2MjMzNA==,5635139,2019-01-14T21:09:49Z,2019-01-14T21:09:49Z,MEMBER,"In an effort to reduce the issue backlog, I'll close this, but please reopen if you disagree","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-424945257,https://api.github.com/repos/pydata/xarray/issues/2227,424945257,MDEyOklzc3VlQ29tbWVudDQyNDk0NTI1Nw==,2443309,2018-09-27T03:16:40Z,2018-09-27T03:16:40Z,MEMBER,"@WeatherGod - are you reading data from netCDF files by chance?

If so, can you share the compression/chunk layout for those (`ncdump -h -s file.nc`)?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-424549023,https://api.github.com/repos/pydata/xarray/issues/2227,424549023,MDEyOklzc3VlQ29tbWVudDQyNDU0OTAyMw==,1217238,2018-09-26T00:54:24Z,2018-09-26T00:54:24Z,MEMBER,@WeatherGod does adding something like `da = da.chunk({'time': 1})` reproduce this with your example?,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-424473282,https://api.github.com/repos/pydata/xarray/issues/2227,424473282,MDEyOklzc3VlQ29tbWVudDQyNDQ3MzI4Mg==,5635139,2018-09-25T19:35:57Z,2018-09-25T19:35:57Z,MEMBER,@WeatherGod do you have a reproducible example? I'm happy to have a look,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-396725591,https://api.github.com/repos/pydata/xarray/issues/2227,396725591,MDEyOklzc3VlQ29tbWVudDM5NjcyNTU5MQ==,1217238,2018-06-12T20:38:47Z,2018-06-12T20:38:47Z,MEMBER,"My measurements:
```
>>> %timeit ds.a.isel(time=time_filter)
1 loop, best of 3: 906 ms per loop
>>> %timeit ds.a.isel(time=time_filter.values)
1 loop, best of 3: 447 ms per loop
>>> %timeit ds.a.values[time_filter]
10 loops, best of 3: 169 ms per loop
```

Given the size of this gap, I suspect this could be improved with some investigation and profiling, but there is certainly an upper-limit on the possible performance gain.

One simple example is that indexing the dataset needs to index both `'a'` and `'time'`, so it's going to be at least twice as slow as only indexing `'a'`. So the second indexing expression `ds.a.isel(time=time_filter.values)` is only `447/(169*2) = 1.32` times slower than the best case scenario. ","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-396675613,https://api.github.com/repos/pydata/xarray/issues/2227,396675613,MDEyOklzc3VlQ29tbWVudDM5NjY3NTYxMw==,1197350,2018-06-12T17:45:48Z,2018-06-12T17:45:48Z,MEMBER,"Another part of the matrix of possibilities. Takes about half the time if you pass `time_filter.values` (numpy array) rather than the `time_filter` DataArray:
```python
%timeit ds.a.isel(time=time_filter.values)
1.3 s ± 67.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-396662676,https://api.github.com/repos/pydata/xarray/issues/2227,396662676,MDEyOklzc3VlQ29tbWVudDM5NjY2MjY3Ng==,5635139,2018-06-12T17:02:34Z,2018-06-12T17:02:34Z,MEMBER,"@rabernat that's a good solution where it's a slice

When is a time that it needs to align a bool array? If you try and pass an array of unequal length, it doesn't work anyway:

```python
In [12]: ds.a.isel(time=time_filter[:-1])

IndexError: Boolean array size 54999999 is used to index array with shape (55000000,).
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890
https://github.com/pydata/xarray/issues/2227#issuecomment-396660606,https://api.github.com/repos/pydata/xarray/issues/2227,396660606,MDEyOklzc3VlQ29tbWVudDM5NjY2MDYwNg==,1197350,2018-06-12T16:55:55Z,2018-06-12T16:55:55Z,MEMBER,"I don't have experience using `isel` with boolean indexing. (Although the docs on [positional indexing](http://xarray.pydata.org/en/latest/indexing.html#positional-indexing) claim it is supported.) My guess is that that the time is being spent aligning the indexer with the array, which is unnecessary since you know they are already aligned. Probably not the most efficient pattern for xarray.

Here's how I would recommend writing the query using label-based selection:
```python
%timeit ds.a.sel(time=slice(50_001, None))
117 ms ± 5.29 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
```

","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331668890